Feature selection method based on Menger curvature and LDA theory for a P300 brain-computer interface

Inter-participant transfer learning with attention based domain adversarial training for P300 detection

A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which requires training. This training process is often time-consuming and inconvenient for new users. In recent years, deep learning models, especially participant-independent models, have garnered significant attention in the domain of ERP classification. However, individual differences in EEG signals hamper model generalization, as the ERP component and other aspects of the EEG signal vary across participants, even when they are exposed to the same stimuli. This paper proposes a novel One-source domain transfer learning method based Attention Domain Adversarial Neural Network (OADANN) to mitigate data distribution discrepancies for cross-participant classification tasks. We train and validate our proposed model on both a publicly available OpenBMI dataset and a Self-collected dataset, employing a leave one participant out cross validation scheme. Experimental results demonstrate that the proposed OADANN method achieves the highest and most robust classification performance and exhibits significant improvements when compared to baseline methods (CNN, EEGNet, ShallowNet, DeepCovNet) and domain generalization methods (ERM, Mixup, and Groupdro). These findings underscore the efficacy of our proposed method.

Transformative Deep Neural Network Approaches in Kidney Ultrasound Segmentation: Empirical Validation with an Annotated Dataset

Kidney ultrasound (US) images are primarily employed for diagnosing different renal diseases. Among them, one is renal localization and detection, which can be carried out by segmenting the kidney US images. However, kidney segmentation from US images is challenging due to low contrast, speckle noise, fluid, variations in kidney shape, and modality artifacts. Moreover, well-annotated US datasets for renal segmentation and detection are scarce. This study aims to build a novel, well-annotated dataset containing 44,880 US images. In addition, we propose a novel training scheme that utilizes the encoder and decoder parts of a state-of-the-art segmentation algorithm. In the pre-processing step, pixel intensity normalization improves contrast and facilitates model convergence. The modified encoder-decoder architecture improves pyramid-shaped hole pooling, cascaded multiple-hole convolutions, and batch normalization. The pre-processing step gradually reconstructs spatial information, including the capture of complete object boundaries, and the post-processing module with a concave curvature reduces the false positive rate of the results. We present benchmark findings to validate the quality of the proposed training scheme and dataset. We applied six evaluation metrics and several baseline segmentation approaches to our novel kidney US dataset. Among the evaluated models, DeepLabv3+ performed well and achieved the highest dice, Hausdorff distance 95, accuracy, specificity, average symmetric surface distance, and recall scores of 89.76%, 9.91, 98.14%, 98.83%, 3.03, and 90.68%, respectively. The proposed training strategy aids state-of-the-art segmentation models, resulting in better-segmented predictions. Furthermore, the large, well-annotated kidney US public dataset will serve as a valuable baseline source for future medical image analysis research.

Upregulation of p300 in paclitaxel-resistant TNBC: implications for cell proliferation via the PCK1/AMPK axis

OBJECTIVE

To explore the role of p300 in the context of paclitaxel (PTX) resistance in triple-negative breast cancer (TNBC) cells, focusing on its interaction with the phosphoenolpyruvate carboxykinase 1 (PCK1)/adenosine monophosphate-activated protein kinase (AMPK) pathway.

METHODS

The expression of p300 and PCK1 at the messenger ribonucleic acid (mRNA) level was detected using a quantitative polymerase chain reaction. The GeneCards and GEPIA databases were used to investigate the relationship between p300 and PCK1. The MDA-MB-231/PTX cell line, known for its PTX resistance, was chosen to understand the specific role of p300 in such cells. The Lipofectamine™ 3000 reagent was used to transfer the p300 small interfering RNA and the overexpression of PCK1 plasmid into MDA-MB-231/PTX. The expression levels of p300, PCK1, 5'AMPK and phosphorylated AMPK (p-AMPK) were determined using the western blot test.

RESULTS

In TNBC cancer tissue, the expression of p300 was increased compared with TNBC paracancerous tissue (P < 0.05). In the MDA-MB-231 cell line of TNBC, the expression of p300 was lower than in the PTX-resistant TNBC cells (MDA-MB-231/PTX) (P < 0.05). The PCK1 expression was decreased in the TNBC cancer tissue compared with TNBC paracancerous tissue, and the PCK1 expression was reduced in MDA-MB-231/PTX than in MDA-MB-231 (P < 0.05) indicating that PCK1 was involved in the resistance function. Additionally, p-AMPK was decreased in MDA-MB-231/PTX compared with MDA-MB-231 (P < 0.05). The adenosine triphosphate (ATP) level was also detected and was significantly lower in MDA-MB-231/PTX than in MDA-MB-231 (P < 0.05). Additionally, cell proliferation increased significantly in MDA-MB-231/PTX at 48 and 72 h (P < 0.05) suggesting that MDA-MB-231/PTX cells obtained the resistance function which was associated with AMPK and ATP level. When p300 was inhibited, p-AMPK and ATP levels elevated in MDA-MB-231/PTX (P < 0.05). When PCK1 was suppressed, the ATP consumption rate decreased, and cell proliferation increased (P < 0.05). However, there were no changes in p300.

CONCLUSIONS

In MDA-MB-231/PTX, p300 can inhibit p-AMPK and ATP levels by inhibiting PCK1 expression. Our findings suggest that targeting p300 could modulate the PCK1/AMPK axis, offering a potential therapeutic avenue for overcoming PTX resistance in TNBC.

A Novel Asynchronous Brain Signals-Based Driver-Vehicle Interface for Brain-Controlled Vehicles

Directly applying brain signals to operate a mobile manned platform, such as a vehicle, may help people with neuromuscular disorders regain their driving ability. In this paper, we developed a novel electroencephalogram (EEG) signal-based driver-vehicle interface (DVI) for the continuous and asynchronous control of brain-controlled vehicles. The proposed DVI consists of the user interface, the command decoding algorithm, and the control model. The user interface is designed to present the control commands and induce the corresponding brain patterns. The command decoding algorithm is developed to decode the control command. The control model is built to convert the decoded commands to control signals. Offline experimental results show that the developed DVI can generate a motion control command with an accuracy of 83.59% and a detection time of about 2 s, while it has a recognition accuracy of 90.06% in idle states. A real-time brain-controlled simulated vehicle based on the DVI was developed and tested on a U-turn road. Experimental results show the feasibility of the DVI for continuously and asynchronously controlling a vehicle. This work not only advances the research on brain-controlled vehicles but also provides valuable insights into driver-vehicle interfaces, multimodal interaction, and intelligent vehicles.

A high-speed hybrid brain-computer interface with more than 200 targets

Objective. Brain-computer interfaces (BCIs) have recently made significant strides in expanding their instruction set, which has attracted wide attention from researchers. The number of targets and commands is a key indicator of how well BCIs can decode the brain's intentions. No studies have reported a BCI system with over 200 targets.Approach. This study developed the first high-speed BCI system with up to 216 targets that were encoded by a combination of electroencephalography features, including P300, motion visual evoked potential (mVEP), and steady-state visual evoked potential (SSVEP). Specifically, the hybrid BCI paradigm used the time-frequency division multiple access strategy to elaborately tag targets with P300 and mVEP of different time windows, along with SSVEP of different frequencies. The hybrid features were then decoded by task-discriminant component analysis and linear discriminant analysis. Ten subjects participated in the offline and online cued-guided spelling experiments. Other ten subjects took part in online free-spelling experiments.Main results.The offline results showed that the mVEP and P300 components were prominent in the central, parietal, and occipital regions, while the most distinct SSVEP feature was in the occipital region. The online cued-guided spelling and free-spelling results showed that the proposed BCI system achieved an average accuracy of 85.37% ± 7.49% and 86.00% ± 5.98% for the 216-target classification, resulting in an average information transfer rate (ITR) of 302.83 ± 39.20 bits min-1and 204.47 ± 37.56 bits min-1, respectively. Notably, the peak ITR could reach up to 367.83 bits min-1.Significance.This study developed the first high-speed BCI system with more than 200 targets, which holds promise for extending BCI's application scenarios.